Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage

Ryota Tanida; Jing Ma; Takashi Nakajima; Mikio Hasegawa; Takahiro Yamamoto; Takumi Ito; Takayuki Kawahara; Atsushi Yamamoto; Noriaki Takahashi; Natsuhiko Sakiyama; Sakuya Kishi; Takayuki Kishimoto; So Hasegawa; Kenjiro Mori; Yoichiro Hashizume

doi:10.1109/APCCAS47518.2019.8953162

Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage

Ryota Tanida, Jing Ma, Takashi Nakajima, Mikio Hasegawa, Takahiro Yamamoto, Takumi Ito, Takayuki Kawahara, Atsushi Yamamoto, Noriaki Takahashi, Natsuhiko Sakiyama, Sakuya Kishi, Takayuki Kishimoto, So Hasegawa, Kenjiro Mori, Yoichiro Hashizume

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

We are finding the location of damage to timber and wooden houses. Two years ago, we succeeded in classifying the damage location with 90% accuracy in a wooden brace house. Last year, we conducted an experiment on a model house in Oita Prefecture and improved the classification rate by preprocessing data. Therefore, we conducted experiments to further improve the classification rate and practical application. The vibration data of the model house in Oita Prefecture was collected using multiple 3-axis acceleration sensors equipped with wireless communication means and monitored at Katsushika Campus, Tokyo University of Science, about 969 km away. By classifying the waveform data by CNN, we succeeded in classifying the damage location and degree of damage with a maximum accuracy of 86.0%.

Original language	English
Title of host publication	Proceedings - APCCAS 2019
Subtitle of host publication	2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	337-340
Number of pages	4
ISBN (Electronic)	9781728129402
DOIs	https://doi.org/10.1109/APCCAS47518.2019.8953162
Publication status	Published - Nov 2019
Event	15th Annual IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2019 - Bangkok, Thailand Duration: 11 Nov 2019 → 14 Nov 2019

Publication series

Name	Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption

Conference

Conference	15th Annual IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2019
Country/Territory	Thailand
City	Bangkok
Period	11/11/19 → 14/11/19

Keywords

CNN
model house
structural health monitoring
wireless communication
wooden house structural damage

Access to Document

10.1109/APCCAS47518.2019.8953162

Cite this

Tanida, R., Ma, J., Nakajima, T., Hasegawa, M., Yamamoto, T., Ito, T., Kawahara, T., Yamamoto, A., Takahashi, N., Sakiyama, N., Kishi, S., Kishimoto, T., Hasegawa, S., Mori, K., & Hashizume, Y. (2019). Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage. In Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption (pp. 337-340). Article 8953162 (Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APCCAS47518.2019.8953162

Tanida, Ryota ; Ma, Jing ; Nakajima, Takashi et al. / Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage. Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 337-340 (Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption).

@inproceedings{10e461d09c984dee93f1dc4a132edc08,

title = "Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage",

abstract = "We are finding the location of damage to timber and wooden houses. Two years ago, we succeeded in classifying the damage location with 90% accuracy in a wooden brace house. Last year, we conducted an experiment on a model house in Oita Prefecture and improved the classification rate by preprocessing data. Therefore, we conducted experiments to further improve the classification rate and practical application. The vibration data of the model house in Oita Prefecture was collected using multiple 3-axis acceleration sensors equipped with wireless communication means and monitored at Katsushika Campus, Tokyo University of Science, about 969 km away. By classifying the waveform data by CNN, we succeeded in classifying the damage location and degree of damage with a maximum accuracy of 86.0%.",

keywords = "CNN, model house, structural health monitoring, wireless communication, wooden house structural damage",

author = "Ryota Tanida and Jing Ma and Takashi Nakajima and Mikio Hasegawa and Takahiro Yamamoto and Takumi Ito and Takayuki Kawahara and Atsushi Yamamoto and Noriaki Takahashi and Natsuhiko Sakiyama and Sakuya Kishi and Takayuki Kishimoto and So Hasegawa and Kenjiro Mori and Yoichiro Hashizume",

year = "2019",

month = nov,

doi = "10.1109/APCCAS47518.2019.8953162",

language = "English",

series = "Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "337--340",

booktitle = "Proceedings - APCCAS 2019",

note = "15th Annual IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2019 ; Conference date: 11-11-2019 Through 14-11-2019",

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Tanida, R, Ma, J, Nakajima, T , Hasegawa, M, Yamamoto, T, Ito, T , Kawahara, T, Yamamoto, A, Takahashi, N, Sakiyama, N, Kishi, S, Kishimoto, T, Hasegawa, S, Mori, K & Hashizume, Y 2019, Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage. in Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption., 8953162, Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption, Institute of Electrical and Electronics Engineers Inc., pp. 337-340, 15th Annual IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2019, Bangkok, Thailand, 11/11/19. https://doi.org/10.1109/APCCAS47518.2019.8953162

Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage. / Tanida, Ryota; Ma, Jing; Nakajima, Takashi et al.
Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption. Institute of Electrical and Electronics Engineers Inc., 2019. p. 337-340 8953162 (Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage

AU - Tanida, Ryota

AU - Ma, Jing

AU - Nakajima, Takashi

AU - Hasegawa, Mikio

AU - Yamamoto, Takahiro

AU - Ito, Takumi

AU - Kawahara, Takayuki

AU - Yamamoto, Atsushi

AU - Takahashi, Noriaki

AU - Sakiyama, Natsuhiko

AU - Kishi, Sakuya

AU - Kishimoto, Takayuki

AU - Hasegawa, So

AU - Mori, Kenjiro

AU - Hashizume, Yoichiro

PY - 2019/11

Y1 - 2019/11

N2 - We are finding the location of damage to timber and wooden houses. Two years ago, we succeeded in classifying the damage location with 90% accuracy in a wooden brace house. Last year, we conducted an experiment on a model house in Oita Prefecture and improved the classification rate by preprocessing data. Therefore, we conducted experiments to further improve the classification rate and practical application. The vibration data of the model house in Oita Prefecture was collected using multiple 3-axis acceleration sensors equipped with wireless communication means and monitored at Katsushika Campus, Tokyo University of Science, about 969 km away. By classifying the waveform data by CNN, we succeeded in classifying the damage location and degree of damage with a maximum accuracy of 86.0%.

AB - We are finding the location of damage to timber and wooden houses. Two years ago, we succeeded in classifying the damage location with 90% accuracy in a wooden brace house. Last year, we conducted an experiment on a model house in Oita Prefecture and improved the classification rate by preprocessing data. Therefore, we conducted experiments to further improve the classification rate and practical application. The vibration data of the model house in Oita Prefecture was collected using multiple 3-axis acceleration sensors equipped with wireless communication means and monitored at Katsushika Campus, Tokyo University of Science, about 969 km away. By classifying the waveform data by CNN, we succeeded in classifying the damage location and degree of damage with a maximum accuracy of 86.0%.

KW - CNN

KW - model house

KW - structural health monitoring

KW - wireless communication

KW - wooden house structural damage

UR - http://www.scopus.com/inward/record.url?scp=85078699831&partnerID=8YFLogxK

U2 - 10.1109/APCCAS47518.2019.8953162

DO - 10.1109/APCCAS47518.2019.8953162

M3 - Conference contribution

T3 - Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption

SP - 337

EP - 340

BT - Proceedings - APCCAS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th Annual IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2019

Y2 - 11 November 2019 through 14 November 2019

ER -

Tanida R, Ma J, Nakajima T , Hasegawa M, Yamamoto T, Ito T et al. Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage. In Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption. Institute of Electrical and Electronics Engineers Inc. 2019. p. 337-340. 8953162. (Proceedings - APCCAS 2019: 2019 IEEE Asia Pacific Conference on Circuits and Systems: Innovative CAS Towards Sustainable Energy and Technology Disruption). doi: 10.1109/APCCAS47518.2019.8953162

Machine Learning Classification Methods using Data of 3-axis Acceleration Sensors equipped with Wireless Communication Means for Locating Wooden House Structural Damage

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